Biomarker Discovery

The Lampe laboratory performs studies to find blood tests or protein biomarkers that can indicate if and where a person has cancer and if it is present how to best treat it. Specifically, we are working to find early detection, recurrence or response biomarkers of colon, breast, pancreas and lung cancer. Useful biomarkers can allow doctors to find and treat cancer earlier and better, saving lives, reducing costs and allowing for “personalized or precision medicine” where the treatment is specific for each person’s disease. Currently our primary approach is to utilize high density antibody arrays to determine proteomic, glycoproteomic and auto-autoantibody markers of disease. We are especially interested in markers where the level of a protein, the level of its glycosylation or whether autoantibodies are produced to it can yield multi-dimensional information on each protein. We consider specific proteins that show consistent cancer-specific changes in 2 or 3 of these measurements to be “hybrid markers”. We hypothesize these markers will suffer less variation between different individuals since one component can act to “standardize” the other measurement.

Our approach to biomarker discovery is unique in several ways. Our discovery arrays contain over 3000 antibodies printed in triplicate giving us reliable and highly consistent data. The fact that we assay proteomic, glycomic and autoantibody changes gives us broad coverage of potential biomarkers. We can screen hundreds of samples in a week reducing false positives. For early detection, we have utilized large pre-diagnostic sample sets from screening cohorts (WHI, CHS, PLCO) reducing the chance that potential early detection biomarkers are not simply related to inflammation or disease burden from people with significant disease burden. We validate in similar sized sample sets and the high denisty of the arrays allow us to test hundreds of candidate biomarkers from our research and others. Only after markers are validated in multiple sets do we spend the effort to convert them to ELISA assays Thus, their utility is more clear reducing costs and prioritizing effort.

We have made excellent progress with biomarkers of lung cancer.  Lung cancer is the leading cause of cancer death, and it can be divided in different subtypes based on histology with the major subdivisions being small cell and non-small cell lung cancer.  Annual low-dose computed tomography (CT) screening has been shown to reduce non-small cell lung cancer mortality. Thus, an annual CT is recommended for heavy smokers. However, CT only detects the presence of nodules, and it is often difficult to tell whether they are benign or malignant. Since many smokers have benign nodules, we need to identify those that are likely malignant, and we have found blood biomarkers that can help with this. Our strategy for non-small cell lung cancer early detection is to combine plasma biomarkers with CT measurements and state-of-the-art CT image analysis methods to better differentiate malignant vs. benign nodules. We are currently validating a decision rule based on the CT and biomarker values that assigns a probability as to whether a nodule is malignant or benign. Benign nodules would be followed in the next annual CT while suspect nodules would be further imaged or biopsied to better classify them. This project is a large collaborative effort with the University of Washington Radiology department as part of the FredHutch Lung Specialized Program of Research Excellence grant from the National Institutes of Health.

Small cell lung cancer is a particularly nasty disease that is uniformly fatal unless it is detected early but annual CT is not effective at reducing mortality. Small cell lung cancer causes production of autoantibodies that can be detected in plasma, and we have recently found a whole new class of these autoantibodies that can be highly predictive of the risk of small cell lung cancer.  Thus, we are studying how they can be used for early detection in a blood sample, or as targeting agents for localizing tumors via or to carry drugs to kill tumors. We are also studying how they could be engineered to be targeting ligands for chimeric antigen receptor T cell killing of tumor. Thus, we envision that a blood test taken at annual screening, if positive could trigger immunoPET imaging to confirm and localize the tumor for surgical removal followed by antibody targeted or CAR-T treatment. This project is funded by a grant from NIH.

Array printer
Our array printer deposits 48 spots 3 times through all of the slides and repeats this 225 times for a total of 10800 spots per slide which represents 3600 different antibodies printed 3 times. This array that was incubated with a case sample labeled with cy5 (red) and a reference sample labeled with cy3 (green).

The 3 array analysis platforms we will utilize include:


 We use our antibody arrays to determine the level of different proteins in a blood or tissue sample by labeling the proteins in the test sample with cy5 and incubating them with a sample of reference proteins labeled with cy3. 

Proteomic antibody array analysis
Proteomic antibody array analysis


Two common cancer specific carbohydrate modifications are sialyl lewis A (the so-called CA19.9 antigen) and lewis X. We can probe the samples applied to the array with fluorescently labeled secondary antibodies that bind to sialyl Lewis A and Lewis X. 

Glycoproteomic antibody array analysis
Glycoproteomic antibody array analysis


The presence of autoantigen-autoantibody pairs in case and control plasma samples applied to arrays can be detected with fluorescently labeled anti-human IgG or IgM.

Autoantibody-autoantigen array analysis
Autoantibody-autoantigen array analysis